{"id":"W4316928357","doi":"10.1158/1055-9965.epi-22-0756","title":"Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model","year":2023,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Cancer Care Ontario; Princess Margaret Cancer Centre; Public Health Ontario; University of Toronto; University Health Network","funders":"National Cancer Institute; NIHR Cambridge Biomedical Research Centre; European Regional Development Fund; European Commission; Fondation du cancer du sein du Québec; National Institutes of Health; National Institute for Health and Care Research; Generalitat de Catalunya; Cancer Research UK; Government of Canada; Centres de Recerca de Catalunya; Instituto de Salud Carlos III; Canadian Institutes of Health Research; Genome Canada","keywords":"Polygenic risk score; Breast cancer; Risk assessment; Medicine; Risk model; Lifetime risk; Cancer; Environmental health; Oncology; Internal medicine; Risk analysis (engineering); Biology; Computer science; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008827065,0.001035088,0.001254678,0.00164906,0.0004194771,0.00186121,0.00213168,0.0008800828,0.004779248],"category_scores_gemma":[0.02077069,0.0005966756,0.001663845,0.001198556,0.0005388765,0.001167686,0.001681994,0.002013284,0.0007167623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376285,"about_ca_system_score_gemma":0.001846229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02143304,"about_ca_topic_score_gemma":0.01827076,"domain_scores_codex":[0.9969407,0.002011111,0.000120927,0.0004982545,0.000251786,0.0001770656],"domain_scores_gemma":[0.9891291,0.008871485,0.0005358257,0.000384574,0.0008415312,0.0002374721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005618878,0.0001998351,0.07039347,0.0001948801,0.001186959,0.0003156511,0.0003104511,0.7985128,0.0004569292,0.01593078,0.002349852,0.1095865],"study_design_scores_gemma":[0.00007068477,0.0001475583,0.006669344,0.00006189229,0.0001906662,0.0001583689,0.00004723584,0.9775864,0.0001495344,0.01265432,0.002218281,0.00004573381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1993591,0.001294467,0.7918488,0.001224259,0.00009839783,0.0003538645,0.001534151,0.001017502,0.003269576],"genre_scores_gemma":[0.8015983,0.000562595,0.1909812,0.0003329183,0.00008463334,0.0006372441,0.001961499,0.0001158196,0.003725807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02143304,"threshold_uncertainty_score":0.04668254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569528402525355,"score_gpt":0.331123046905329,"score_spread":0.3054277628800754,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}